Several algorithms are commonly used in pathway analysis, each with its unique strengths and limitations:
1. Over-Representation Analysis (ORA): This method assesses whether a specific set of genes is over-represented in a predefined list of pathways. Tools like DAVID and GSEA use this approach. 2. Gene Set Enrichment Analysis (GSEA): GSEA evaluates whether a set of genes shows statistically significant, concordant differences between two biological states (e.g., cancer vs. normal). It is particularly useful for analyzing gene expression data. 3. Pathway Topology-based Analysis: This approach considers the network structure of pathways. Algorithms like SPIA (Signaling Pathway Impact Analysis) evaluate both the over-representation of genes and the perturbation of pathway topology. 4. Network-based Methods: These methods involve constructing interaction networks (e.g., protein-protein interaction networks) and identifying subnetworks or modules that are significantly altered in cancer. Tools like STRING and Cytoscape are often used.
